Anomalies are any irregular or unexpected patterns within a data set. The detection of anomalies is required in many situations in which large amounts of time-variant data are available. For example, detection of telecommunications fraud, detection of credit card fraud, encryption key management systems and early problem identification.
One problem is that known anomaly detectors and methods of anomaly detection are designed for used with only one such situation. They cannot easily be used in other situations. Each anomaly detection situation involves a specific type of data and specific sources and formats for that data. An anomaly detector designed for one situation works specifically for a certain type, source and format of data and it is difficult to adapt the anomaly detector for use in another situation. Known methods of adapting an anomaly detector for used in a new situation have involved carrying out this adaptation manually. This is a lengthy and expensive task requiring specialist knowledge not only of the technology involved in the anomaly detector but also of the application domains involved. The risk of errors being made is also high.
Another problem is that a particular method of anomaly detection is often most suitable for one particular situation. This means that transfer of a particular anomaly detector to a new situation may not be appropriate unless core elements of the anomaly detector method and/or apparatus are adapted. This is particularly time consuming and expensive particularly as the development of a new anomaly detector from scratch may often be necessary.
One application for anomaly detection is the detection of telecommunications fraud. Telecommunications fraud is a multi-billion dollar problem around the world. Anticipated losses are in excess of $1 billion a year in the mobile market alone. For example, the Cellular Telecoms Industry Association estimate that in 1996 the cost to U.S. carriers of mobile phone fraud alone is $1.6 million per day, projected to rise to $2.5 million per day by 1997. This makes telephone fraud an expensive operating cost for every telephone service provider in the world. Because the telecommunications market is expanding rapidly the problem of telephone fraud is set to become larger.
Most telephone operators have some defence against fraud already in place. These are risk limitation tools such as simple aggregation of call-attempts, credit checking and tools to identify cloning, or tumbling. Cloning occurs where the fraudster gains access to the network by emulating or copying the identification code of a genuine telephone. This results in a multiple occurrence of the telephone unit. Tumbling occurs where the fraudster emulates or copies the identification codes of several different genuine telephone units.
Methods have been developed to detect each of these particular types of fraud. However, new types of fraud are continually evolving and it is difficult for service providers to keep “one-step ahead” of the fraudsters. Also, the known methods of detecting fraud are often based on simple strategies which can easily be defeated by clever thieves who realise what fraud-detection techniques are being used against them.
A number of rule-based systems have been developed, however, they have a series of limitations. It is now being acknowledged that each corporate and individual customer will show different behaviour, and thus a simple set of rules is insufficient to adequately monitor network traffic. To adapt these rule-based systems to allow each customer to have their own unique thresholds in not possible due to the sheer volumes of data involved.
There are a number of difficulties with identifying fraud, namely:                Fraud is dynamic by nature; fraudulent behaviour will change over time.        The size of the problem area is vast, due to the number of users on a network, and the number of calls made.        Rapid identification of fraud is needed; losses from a given case of fraud tend to grow exponentially.        Some forms of fraud are particularly costly and should therefore be the subject of special attention e.g. international phone calls.        Customer transparency; a customer should not see the fraud detection system in action.        
Another method of detecting telecommunications fraud involves using neural network technology. One problem with the use of neural networks to detect anomalies in a data set lies in pre-processing the information to input to the neural network. The input information needs to be represented in a way which captures the essential features of the information and emphasises these in a manner suitable for use by the neural network itself. The neural network needs to detect fraud efficiently without wasting time maintaining and processing redundant information or simply detecting “noise” in the data. At the same time the neural network needs enough information to be able to detect many different types of fraud including types of fraud which may evolve in the future. As well as this the neural network should be provided with information in a way that it is able to allow for legitimate changes in behaviour and not identify these as potential frauds.
A particular problem for any known method of detecting fraud is that both static classification and temporal prediction are required. That is, anomalous use has to be classified as such, but only in relation to an emerging temporal pattern. Over a period of time an individual phone will generate a macroscopic pattern of use, in which , for example, intercontinental calls may be rare; however within this overall pattern there will inevitably be violations —on a particular day the phone may be used for several intercontinental calls. A pattern of behaviour may only be anomalous relative to the historical pattern of behaviour.
Another problem is that a particular type of information to be analysed by a neural network is often in a variety of formats. For example, information about individual telephone calls is typically contained in call detail records. The content and format of call detail records differs for different telecommunications systems and this makes it difficult for such information to be input directly to a neural network based system.
A further problem is that once information has been provided for input to a neural network based system it is often not suitable for other purposes. For example, when a neural network system is being used to detect fraudsters much information about the behaviour of customers is prepared for input to the system. This information could also be used for marketing purposes to develop a much more detailed understanding of customer behaviour. However, this is often not easy to effect because of the format of the data.
One problem with known methods of fraud detection is that they are often unable to cope adequately with natural changes in the input data. For example, a customer's telephone call behaviour may change legitimately over time; the customer may travel abroad and make more long distance calls. This should not be detected as an anomaly and be classified as a potential fraud. Because the telecommunications market size is increasing, this is a particular problem for fraud detection in telecommunications.
Known methods of anomaly or fraud detection which have used neural networks involve first training the neural network with a training data set. Once the training phase is over the neural network is used to process telecoms data in order to identify fraud candidates. As the behaviour of customers evolves, new data input to the neural network may be widely different from the original training data set. In these circumstances the neural network may identify legitimate new patterns in the data as anomalies. Similarly, real cases of fraud may go unidentified. In this situation it is necessary to retrain the neural network using an updated training data set which is updated to reflect new features of the data.
Several problems arise as a result of this need for retraining. For example, a decision needs to be made about when to retrain. Typically this complex decision is made by the user who requires specialist knowledge not only about telecoms fraud but also about the neural network system. Because telecoms fraud is an on-going problem which takes place 24 hours a day, 7 days a week, it is often not possible for an expert user to be available. This means that the system may “under perform” for some time before retraining is initiated.
Another problem is that the performance of the neural network system needs to be monitored in order to determine when the system is “Sunder performing”. This can be a difficult and lengthy task which takes up valuable time.
Another problem is that the process of retraining is itself a lengthy and computationally expensive process. Whilst retraining is in progress it is not possible to use the neural network system to detect anomalies. This means that telecoms fraud may go undetected during the retraining phase. Also, the retraining process may take up valuable processing resources which are required for other tasks. This is especially important in the field of telecommunications where it may be required to site the neural network system at a busy switch or node in the telecommuncations network.
A further problem is that intervention and input from the user is typically required during the retraining process. This can be inconvenient when it is necessary to retrain quickly and also requires a trained user to be available.